entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
WingLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | ZephyrII/mmpose_charger | WingLoss | false | 12,020 | [
"Apache-2.0"
] | 0 | ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd | https://github.com/ZephyrII/mmpose_charger/tree/ca5f7ab439ae40c4ceab2c6fd1d58112dc0ea7cd |
ResNet_conv1 | import math
import torch
import torch.utils.data
import torch.nn as nn
class ResNet_conv1(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet_conv1, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3,
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.utils.data
import torch.nn as nn
assert_size_stride = t... | donegaci/memc-net | ResNet_conv1 | false | 15,211 | [
"MIT"
] | 145 | 9bdb0ab6ce99af22a165db2cedacd148dd6083c0 | https://github.com/donegaci/memc-net/tree/9bdb0ab6ce99af22a165db2cedacd148dd6083c0 |
DecoderBlock | import torch
import torch.utils.data
import torch.nn as nn
import torch.onnx
import torch.autograd
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block."""
def __init__(self, num_in, num_out):
super().__init__()
self.block = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CorentinLemaitre/robosat.pink | DecoderBlock | false | 5,031 | [
"MIT"
] | 1 | 6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 | https://github.com/CorentinLemaitre/robosat.pink/tree/6ec29a4dd4c0cbf953e73818d7338ee68b2451d3 |
NPIProg | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | nienjiuntai/pytorch-npi | NPIProg | false | 4,089 | [
"MIT"
] | 0 | 16b413c152dfb7f1506a85997adc10ddc2d9af35 | https://github.com/nienjiuntai/pytorch-npi/tree/16b413c152dfb7f1506a85997adc10ddc2d9af35 |
GlobalAvgPool2d | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | Shadowalker1995/few-shot | GlobalAvgPool2d | false | 9,436 | [
"MIT"
] | 0 | 68026f4d5d092b9cb7cc3b50ba8d28ca1b70ade9 | https://github.com/Shadowalker1995/few-shot/tree/68026f4d5d092b9cb7cc3b50ba8d28ca1b70ade9 |
SpatialPyramidPooling | import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | IDayday/YOLOv4_CAM | SpatialPyramidPooling | false | 8,296 | [
"Apache-2.0"
] | 34 | 8df61f1c59c197126f0385c1ec1cf65a29a80cec | https://github.com/IDayday/YOLOv4_CAM/tree/8df61f1c59c197126f0385c1ec1cf65a29a80cec |
BothContextGate | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | LeeeeoLiu/OpenNMT-py | BothContextGate | false | 2,503 | [
"MIT"
] | 0 | 9be3a8951e9181aabe5440e4ea98173b7e749b5c | https://github.com/LeeeeoLiu/OpenNMT-py/tree/9be3a8951e9181aabe5440e4ea98173b7e749b5c |
LRN | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.optim
import torch.nn as nn
assert_size_stride = torch._C._dynamo.... | Fyy10/UESTC-Thesis-DA | LRN | false | 473 | [
"MIT"
] | 0 | 6cb16efd1f80aa569c90874a806a62dec8afaec4 | https://github.com/Fyy10/UESTC-Thesis-DA/tree/6cb16efd1f80aa569c90874a806a62dec8afaec4 |
UNETMin | import torch
from torch import nn
class UNETMin(nn.Module):
"""UNET Without concatenation during decoding"""
def __init__(self):
super(UNETMin, self).__init__()
self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=16,
kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.C... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | quenting44/semantic_segmentation | UNETMin | false | 10,829 | [
"MIT"
] | 0 | bd197ddda3c6891d69ff7e552a0c224c7ec1269a | https://github.com/quenting44/semantic_segmentation/tree/bd197ddda3c6891d69ff7e552a0c224c7ec1269a |
PixBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | MohamadHMousavi/demo_wsi_superres | PixBlock | false | 11,844 | [
"MIT"
] | 0 | 7e846470aa228affa62ea77c38c138dde087a0de | https://github.com/MohamadHMousavi/demo_wsi_superres/tree/7e846470aa228affa62ea77c38c138dde087a0de |
Mish | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch.nn import Module
import torch.optim
assert_size_str... | PABannier/nanograd | Mish | false | 8,609 | [
"MIT"
] | 18 | 5acd355c638885cbfc0fd0f1c4903964e7fb7de9 | https://github.com/PABannier/nanograd/tree/5acd355c638885cbfc0fd0f1c4903964e7fb7de9 |
LayerNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._d... | FUTUREEEEEE/S2R-DepthNet | LayerNorm | false | 2,248 | [
"MIT"
] | 0 | 415cc40aef10f9540026ff435d14a9ba9e30ad74 | https://github.com/FUTUREEEEEE/S2R-DepthNet/tree/415cc40aef10f9540026ff435d14a9ba9e30ad74 |
ModulatedConv2d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd... | jchetboun/anycost-gan | ModulatedConv2d | false | 10,396 | [
"MIT"
] | 0 | 7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 | https://github.com/jchetboun/anycost-gan/tree/7e0005e50b915e2dfeb90fe7a9846c5df38d7c06 |
SelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | aisolab/nlp_implementation | SelfAttention | false | 14,766 | [
"MIT"
] | 181 | 21ea6e3f5737e7074bdd8dd190e5f5172f86f6bf | https://github.com/aisolab/nlp_implementation/tree/21ea6e3f5737e7074bdd8dd190e5f5172f86f6bf |
MultiHeadSelfAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | TimSYQQX/glyce | MultiHeadSelfAttention | false | 14,514 | [
"Apache-2.0"
] | 396 | 1542ed30ce104c25aa5c69ffcc9cc5ef2fcda975 | https://github.com/TimSYQQX/glyce/tree/1542ed30ce104c25aa5c69ffcc9cc5ef2fcda975 |
JointMseLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guard... | Lysemo/StructureAwareDCNN | JointMseLoss | false | 17,626 | [
"Apache-2.0"
] | 6 | f2437d39eb246ac6cd9e63b44070f1aca8838475 | https://github.com/Lysemo/StructureAwareDCNN/tree/f2437d39eb246ac6cd9e63b44070f1aca8838475 |
TSA_Fusion | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | creeper121386/EDVR-modified | TSA_Fusion | false | 1,816 | [
"Apache-2.0"
] | 0 | 3fa565b99811e8f84f6ea3793090614606382332 | https://github.com/creeper121386/EDVR-modified/tree/3fa565b99811e8f84f6ea3793090614606382332 |
SoftDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch._C
import torch.serialization
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strid... | dkswxd/Swin-Transformer-Semantic-Segmentation | SoftDiceLoss | false | 1,855 | [
"Apache-2.0"
] | 0 | 6af19736e5492a01d8952d4ee86a6d59b21c2ae1 | https://github.com/dkswxd/Swin-Transformer-Semantic-Segmentation/tree/6af19736e5492a01d8952d4ee86a6d59b21c2ae1 |
down | import torch
from torch.functional import F
import torch.nn as nn
import torch.nn.functional as F
class down(nn.Module):
"""
A class for creating neural network blocks containing layers:
Average Pooling --> Convlution + Leaky ReLU --> Convolution + Leaky ReLU
This is used in the UNet Class t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | samuelpietri/Super-SloMo | down | false | 4,264 | [
"MIT"
] | 0 | e20eaa5550c30737be42b61f8e82e731cfd17457 | https://github.com/samuelpietri/Super-SloMo/tree/e20eaa5550c30737be42b61f8e82e731cfd17457 |
EDMLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ankerok1/nima.pytorch | EDMLoss | false | 14,867 | [
"MIT"
] | 300 | bbdbeeb8c22d880205a4fa35cfc2a533d064ee5d | https://github.com/ankerok1/nima.pytorch/tree/bbdbeeb8c22d880205a4fa35cfc2a533d064ee5d |
RelPositionMultiHeadedAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | pengchengguo/wenet | RelPositionMultiHeadedAttention | false | 16,254 | [
"Apache-2.0"
] | 1,166 | 940dc164e5cfa9b8c0131688f0f9457af9563892 | https://github.com/pengchengguo/wenet/tree/940dc164e5cfa9b8c0131688f0f9457af9563892 |
DPRNNCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | bogdan-kulynych/opacus | DPRNNCell | false | 3,241 | [
"Apache-2.0"
] | 0 | e2d13003a179f64920835bc585f3729b8148279f | https://github.com/bogdan-kulynych/opacus/tree/e2d13003a179f64920835bc585f3729b8148279f |
Accuracy | from torch.nn import Module
import torch
from torch import Tensor
class Accuracy(Module):
"""
Class for calculating the accuracy for a given prediction and the labels
for comparison.
Expects the inputs to be from a range of 0 to 1 and sets a crossing threshold at 0.5
the labels are similarly round... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
from torch import Tensor
assert_size_stride = torch._C._dynam... | vixadd/sparseml | Accuracy | false | 4,501 | [
"Apache-2.0"
] | 0 | e2dcb66bad713542158dfe54cba113a0cc02ed39 | https://github.com/vixadd/sparseml/tree/e2dcb66bad713542158dfe54cba113a0cc02ed39 |
RegLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | XiangLiK/cv_course | RegLoss | false | 18,114 | [
"MIT"
] | 8 | da7c2318fd4128bbdab96db26ddbb2524f37d0a0 | https://github.com/XiangLiK/cv_course/tree/da7c2318fd4128bbdab96db26ddbb2524f37d0a0 |
AE_3D_50_no_last_bias | import torch
import torch.nn as nn
import torch.utils.data
class AE_3D_50_no_last_bias(nn.Module):
def __init__(self, n_features=4):
super(AE_3D_50_no_last_bias, self).__init__()
self.en1 = nn.Linear(n_features, 50)
self.en2 = nn.Linear(50, 50)
self.en3 = nn.Linear(50, 20)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_3D_50_no_last_bias | false | 12,439 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
NonLinearProbe1 | import torch
from torch import nn
class NonLinearProbe1(nn.Module):
def __init__(self, input_dim, num_classes=255):
super().__init__()
self.linear = nn.Linear(in_features=input_dim, out_features=num_classes
)
self.relu = nn.ReLU()
def forward(self, feature_vectors):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | PAL-ML/atari-representation-learning | NonLinearProbe1 | false | 2,803 | [
"MIT"
] | 0 | 11977da174d9ef74c0b2333322b9f0b28e15239e | https://github.com/PAL-ML/atari-representation-learning/tree/11977da174d9ef74c0b2333322b9f0b28e15239e |
Gblock | import torch
import torch.nn as nn
class Gblock(nn.Module):
def __init__(self, in_channels, out_channels, groups):
super(Gblock, self).__init__()
self.conv0 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
padding=1, groups=groups)
self.relu = nn.PReLU()
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Bovbene/WSCCSN | Gblock | false | 174 | [
"Apache-2.0"
] | 0 | 7f454050218e7f2162b0bdc1cdff938d876efc0b | https://github.com/Bovbene/WSCCSN/tree/7f454050218e7f2162b0bdc1cdff938d876efc0b |
AddPositionalEncoding | import torch
import torch.nn as nn
import torch.onnx
class AddPositionalEncoding(nn.Module):
def __init__(self, hidden_size, max_sequence_length):
super(AddPositionalEncoding, self).__init__()
self.hidden_size = hidden_size
self.max_sequence_length = max_sequence_length
self.posit... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.onnx
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynam... | jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch | AddPositionalEncoding | false | 3,770 | [
"MIT"
] | 0 | d27d2d390f0831330405c16bd29c7f331ad2007a | https://github.com/jonndoe/Character-Level-Language-Modeling-with-Deeper-Self-Attention-pytorch/tree/d27d2d390f0831330405c16bd29c7f331ad2007a |
LanguageModelCriterion | import torch
import torch.nn as nn
from torch.autograd import *
def to_contiguous(tensor):
if tensor.is_contiguous():
return tensor
else:
return tensor.contiguous()
class LanguageModelCriterion(nn.Module):
def __init__(self):
super(LanguageModelCriterion, self).__init__()
d... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.autograd import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | ifty1987/PORL | LanguageModelCriterion | false | 15,590 | [
"MIT"
] | 61 | 979d5462b5c74bcca8013d9c54d86b676d3e2d43 | https://github.com/ifty1987/PORL/tree/979d5462b5c74bcca8013d9c54d86b676d3e2d43 |
GlobalAvgPool | import torch
import torch.nn as nn
class GlobalAvgPool(nn.Module):
def forward(self, x):
N, C = x.size(0), x.size(1)
return x.view(N, C, -1).mean(dim=2)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | DaShi-Git/simsg | GlobalAvgPool | false | 13,536 | [
"Apache-2.0"
] | 58 | 31df608cd04facb2b8b546cc6f53d84716117bdf | https://github.com/DaShi-Git/simsg/tree/31df608cd04facb2b8b546cc6f53d84716117bdf |
Descendant | import torch
from torch import nn
import torch.nn.functional as F
class Descendant(nn.Module):
"""Descendant descendantEncoder model for ADDA."""
def __init__(self):
"""Init Descendant descendantEncoder."""
super(Descendant, self).__init__()
self.restored = False
self.conv1 = ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | lindagaw/Kadara | Descendant | false | 10,471 | [
"MIT"
] | 0 | f1059b69a581344ca460c8df02ac3f73f3fbcba1 | https://github.com/lindagaw/Kadara/tree/f1059b69a581344ca460c8df02ac3f73f3fbcba1 |
Block | import torch
from torch import nn
import torch.nn.functional as F
class Block(nn.Module):
def __init__(self, dim):
super(Block, self).__init__()
self.dim = dim
self.layer_norm = nn.LayerNorm(self.dim)
self.conv = nn.Conv1d(self.dim, self.dim, kernel_size=3, padding=1)
def for... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JiwanChung/tapm | Block | false | 8,379 | [
"MIT"
] | 14 | ec42b139d1c012daccc55f85e67744488d526476 | https://github.com/JiwanChung/tapm/tree/ec42b139d1c012daccc55f85e67744488d526476 |
GatAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = to... | AlexMinhao/NAS_GNN | GatAttention | false | 17 | [
"Apache-2.0"
] | 0 | 89183988a96e1d6baed910ab3843c13282f8b077 | https://github.com/AlexMinhao/NAS_GNN/tree/89183988a96e1d6baed910ab3843c13282f8b077 |
EnsembleFC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | simonat2011/DI-engine | EnsembleFC | false | 12,987 | [
"Apache-2.0"
] | 0 | 3a91c4297d58b3beff40b48bd37eb0b399c724a7 | https://github.com/simonat2011/DI-engine/tree/3a91c4297d58b3beff40b48bd37eb0b399c724a7 |
ContrastiveLoss | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim
class ContrastiveLoss(nn.Module):
def __init__(self, margin=2.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pair... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import... | ani0075/learning-pytorch | ContrastiveLoss | false | 1,446 | [
"MIT"
] | 0 | c50e4126751821075e94c4cc3950dd8780370fce | https://github.com/ani0075/learning-pytorch/tree/c50e4126751821075e94c4cc3950dd8780370fce |
NasAvgPoolBlock | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | earhian/imgclsmob | NasAvgPoolBlock | false | 6,625 | [
"MIT"
] | 1 | c87c0942420876941868c016211073dec4392e4d | https://github.com/earhian/imgclsmob/tree/c87c0942420876941868c016211073dec4392e4d |
DiffLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | lyh512796310/MMSA | DiffLoss | false | 3,953 | [
"MIT"
] | 0 | e1735afd1b4e763995ab7aacb001884a7b7146ff | https://github.com/lyh512796310/MMSA/tree/e1735afd1b4e763995ab7aacb001884a7b7146ff |
CO_Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | huanglf714/COMatchNet | CO_Attention | false | 6,858 | [
"Apache-2.0"
] | 1 | 79023f5be65d354eb9bdac026d7e0d73110bc4aa | https://github.com/huanglf714/COMatchNet/tree/79023f5be65d354eb9bdac026d7e0d73110bc4aa |
multi_scale_spatial | import torch
import torch.nn as nn
class multi_scale_spatial(nn.Module):
def __init__(self, limb_blocks):
super(multi_scale_spatial, self).__init__()
(self.left_arm, self.right_arm, self.left_leg, self.right_leg, self
.head_spine) = limb_blocks
self.maxpool1 = nn.AdaptiveMaxPo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | fabro66/Online-Skeleton-based-Action-Recognition | multi_scale_spatial | false | 15,338 | [
"MIT"
] | 63 | de00cbf17ceea98a7d07f68bbbd966bfd02d3b40 | https://github.com/fabro66/Online-Skeleton-based-Action-Recognition/tree/de00cbf17ceea98a7d07f68bbbd966bfd02d3b40 |
EdgeFeatures | import torch
import torch.nn as nn
class EdgeFeatures(nn.Module):
"""Convnet features for edges.
e_ij = U*e_ij + V*(x_i + x_j)
"""
def __init__(self, hidden_dim):
super(EdgeFeatures, self).__init__()
self.U = nn.Linear(hidden_dim, hidden_dim, True)
self.V = nn.Linear(hidden_d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | BrandonKates/graph-convnet-tsp | EdgeFeatures | false | 11,247 | [
"MIT"
] | 0 | f6e17e84311c23fd5cab041b7a27b4e0636c44f8 | https://github.com/BrandonKates/graph-convnet-tsp/tree/f6e17e84311c23fd5cab041b7a27b4e0636c44f8 |
TorchTensorAttrNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn.functional
assert_size_stride = torch._C._dynamo.guards.assert_s... | elad-c/model_optimization | TorchTensorAttrNet | false | 10,659 | [
"Apache-2.0"
] | 0 | b0ecf41c3f9434008d57d7fe724ff8585e19d4cc | https://github.com/elad-c/model_optimization/tree/b0ecf41c3f9434008d57d7fe724ff8585e19d4cc |
CenterIntersection | import torch
import torch.nn as nn
import torch.nn.functional as F
class CenterIntersection(nn.Module):
def __init__(self, dim):
super(CenterIntersection, self).__init__()
self.dim = dim
self.layers = nn.Parameter(torch.zeros(self.dim * 2 + 2, self.dim))
nn.init.xavier_uniform_(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | google-research/smore | CenterIntersection | false | 15,454 | [
"Apache-2.0"
] | 78 | e4ba95a7466ef7d018987bce7688b77bf2ea7e4f | https://github.com/google-research/smore/tree/e4ba95a7466ef7d018987bce7688b77bf2ea7e4f |
AE_2D_v100 | import torch
import torch.nn as nn
import torch.utils.data
class AE_2D_v100(nn.Module):
def __init__(self, n_features=4):
super(AE_2D_v100, self).__init__()
self.en1 = nn.Linear(n_features, 100)
self.en2 = nn.Linear(100, 100)
self.en3 = nn.Linear(100, 100)
self.en4 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | gitter-badger/HEPAutoencoders | AE_2D_v100 | false | 12,438 | [
"Apache-2.0"
] | 0 | 43010cd66fa4335a04b30b87926148e1c8d92de9 | https://github.com/gitter-badger/HEPAutoencoders/tree/43010cd66fa4335a04b30b87926148e1c8d92de9 |
Critic | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
from torch... | tjkemp/tennis-example | Critic | false | 13,041 | [
"MIT"
] | 0 | 3cb0c52a93c65f88872cf44e3782bf87d9d8cef3 | https://github.com/tjkemp/tennis-example/tree/3cb0c52a93c65f88872cf44e3782bf87d9d8cef3 |
CustomizedLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | B06901052/Torch-Pruning | CustomizedLayer | false | 11,235 | [
"MIT"
] | 0 | 43c99e1ea6039c7641e01cd7527492d69bfce35a | https://github.com/B06901052/Torch-Pruning/tree/43c99e1ea6039c7641e01cd7527492d69bfce35a |
mlp_3layer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Mahoumaru/auto_LiRPA | mlp_3layer | false | 11,680 | [
"BSD-3-Clause"
] | 0 | b03a6c36eb1b921726778359d6d2b94e0cd7e480 | https://github.com/Mahoumaru/auto_LiRPA/tree/b03a6c36eb1b921726778359d6d2b94e0cd7e480 |
TensorClampMax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | Ilyabasharov/torch2trt | TensorClampMax | false | 2,547 | [
"MIT"
] | 0 | 76bf298b3da408509665e23e2494922b131afb10 | https://github.com/Ilyabasharov/torch2trt/tree/76bf298b3da408509665e23e2494922b131afb10 |
GaussianFocalLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Huuush/deepfashion2-det | GaussianFocalLoss | false | 11,487 | [
"Apache-2.0"
] | 0 | 46af0ada8d6f534de2de6a9c069580cd1bf609ec | https://github.com/Huuush/deepfashion2-det/tree/46af0ada8d6f534de2de6a9c069580cd1bf609ec |
Actor | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | cugzj/Adaptive-B | Actor | false | 6,519 | [
"Apache-2.0"
] | 1 | cebc965b1dbad93332ae371bfef8640259d940c4 | https://github.com/cugzj/Adaptive-B/tree/cebc965b1dbad93332ae371bfef8640259d940c4 |
Upsample | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Leo-xxx/lighttrack | Upsample | false | 5,503 | [
"MIT"
] | 1 | bc12f53c621c42038066a1af7499838b571b0c76 | https://github.com/Leo-xxx/lighttrack/tree/bc12f53c621c42038066a1af7499838b571b0c76 |
FeedForward | from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
class FeedForward(Module):
"""
## FFN module
"""
def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1,
activation=nn.ReLU(), is_gated: 'bool'=... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
f... | Aarsh2001/annotated_deep_learning_paper_implementations | FeedForward | false | 4,776 | [
"MIT"
] | 1 | ff0d5c065da1a46769f5f66fddc252c178f8fa37 | https://github.com/Aarsh2001/annotated_deep_learning_paper_implementations/tree/ff0d5c065da1a46769f5f66fddc252c178f8fa37 |
BasicModel | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | LMdeLiangMi/captum | BasicModel | false | 5,459 | [
"BSD-3-Clause"
] | 1 | 8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 | https://github.com/LMdeLiangMi/captum/tree/8bd9686013fe0ba8996e9b1cbeb0ea8e91512787 |
GraphConvolution | import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.3):
super(GraphConvolution, self).__init__()
self.in_feat... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Yottaxx/T-LSTM | GraphConvolution | false | 18,165 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
ExgLayer | import torch
import torch.nn as nn
class ExgLayer(nn.Module):
def __init__(self, x_size, h_size, g_size, out_size):
super(ExgLayer, self).__init__()
self.h_size = h_size
self.g_size = g_size
self.out_size = out_size
self.x_size = x_size
self.linear_x2 = nn.Linear(x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Yottaxx/T-LSTM | ExgLayer | false | 18,152 | [
"MIT"
] | 9 | 92618d8c3ee2418b194a2e1592512548da955b77 | https://github.com/Yottaxx/T-LSTM/tree/92618d8c3ee2418b194a2e1592512548da955b77 |
GCNLayer | import torch
import torch.nn as nn
import torch.utils.data
class GCNLayer(nn.Module):
def __init__(self, embed_size, dropout=0.0):
super().__init__()
self.embed_size = embed_size
self.ctx_layer = nn.Linear(self.embed_size, self.embed_size, bias=False
)
self.layernorm =... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | Roc-Ng/HANet | GCNLayer | false | 8,710 | [
"MIT"
] | 34 | e679703e9e725205424d87f750358fb4f62ceec5 | https://github.com/Roc-Ng/HANet/tree/e679703e9e725205424d87f750358fb4f62ceec5 |
SH2Signal | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
from scipy import special as sci
assert... | SimonKoppers/DELIMIT | SH2Signal | false | 17,942 | [
"MIT"
] | 7 | d778a567bbec1beef2395ead60aa1e30086bb07c | https://github.com/SimonKoppers/DELIMIT/tree/d778a567bbec1beef2395ead60aa1e30086bb07c |
Contract | import torch
import torch.nn as nn
import torch.utils.data
class Contract(nn.Module):
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size()
s = self.gain
x = x.view(N, C, H // s, s, W // s, s)
x = x.permute... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | ChaokunChang/SVAS | Contract | false | 242 | [
"Apache-2.0"
] | 0 | 61af6eb39269edff8ea5147311628b3200c3a3d2 | https://github.com/ChaokunChang/SVAS/tree/61af6eb39269edff8ea5147311628b3200c3a3d2 |
ScaleNorm | import torch
import torch.nn as nn
class ScaleNorm(nn.Module):
"""ScaleNorm"""
def __init__(self, scale, eps=1e-05):
super(ScaleNorm, self).__init__()
self.scale = scale
self.eps = eps
def forward(self, x):
norm = self.scale / torch.norm(x, dim=1, keepdim=True).clamp(min=... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | nvski/ST-TR | ScaleNorm | false | 4,100 | [
"MIT"
] | 0 | 75aa9fb872af217f8616c01cee7ca6548846260b | https://github.com/nvski/ST-TR/tree/75aa9fb872af217f8616c01cee7ca6548846260b |
_GRU_ODE | import torch
class _GRU_ODE(torch.nn.Module):
def __init__(self, input_channels, hidden_channels):
super(_GRU_ODE, self).__init__()
self.input_channels = input_channels
self.hidden_channels = hidden_channels
self.W_r = torch.nn.Linear(input_channels, hidden_channels, bias=False)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | athon-millane/NeuralCDE | _GRU_ODE | false | 12,126 | [
"Apache-2.0"
] | 0 | 4196890fe5bf7a69925a12ff35e86f212963be71 | https://github.com/athon-millane/NeuralCDE/tree/4196890fe5bf7a69925a12ff35e86f212963be71 |
ArctanLayer | import torch
import torch.nn as nn
import torch.nn
from abc import ABCMeta
from abc import abstractmethod
class Layer(nn.Module, metaclass=ABCMeta):
def __init__(self):
super(Layer, self).__init__()
@abstractmethod
def forward(self, x):
"""
>>> do forward pass with a given input
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn
from abc import ABCMeta
from abc import a... | Isaac-Li-cn/certify_robustness | ArctanLayer | false | 568 | [
"BSD-3-Clause"
] | 0 | f904dc923afc6354e406c57a1c923d13fc39d315 | https://github.com/Isaac-Li-cn/certify_robustness/tree/f904dc923afc6354e406c57a1c923d13fc39d315 |
CriterionCWD | import torch
import torch.nn as nn
import torch._utils
import torch.optim
class ChannelNorm(nn.Module):
def __init__(self):
super(ChannelNorm, self).__init__()
def forward(self, featmap):
n, c, _h, _w = featmap.shape
featmap = featmap.reshape((n, c, -1))
featmap = featmap.sof... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | yubin1219/Semantic-Seg | CriterionCWD | false | 4,642 | [
"BSD-2-Clause"
] | 0 | c40bd43d3d7e44bc995b8d041736580dec084251 | https://github.com/yubin1219/Semantic-Seg/tree/c40bd43d3d7e44bc995b8d041736580dec084251 |
BiAttention | import torch
from torchvision.transforms import functional as F
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class FCNet(nn.Module):
def __init__(self, in_size, out_size, activate=None, drop=0.0):
super(FCNet, self).__init__()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | zhanwenchen/Scene-Graph-Benchmark.pytorch | BiAttention | false | 4,666 | [
"MIT"
] | 0 | c86475bcbdaefcc1656a2890194355c2b32aa694 | https://github.com/zhanwenchen/Scene-Graph-Benchmark.pytorch/tree/c86475bcbdaefcc1656a2890194355c2b32aa694 |
ClassificationModel | import torch
import torch.nn as nn
class ClassificationModel(nn.Module):
def __init__(self, num_features_in, num_anchors=9, num_classes=80,
prior=0.01, feature_size=256, dropout1=0.25, dropout2=0.25):
super(ClassificationModel, self).__init__()
self.num_classes = num_classes
self.... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | TobyChen0106/DeepQ_Final_B05901170 | ClassificationModel | false | 1,152 | [
"Apache-2.0"
] | 0 | 808a224c01272726a051eb7b7bb9e1b28887716e | https://github.com/TobyChen0106/DeepQ_Final_B05901170/tree/808a224c01272726a051eb7b7bb9e1b28887716e |
TorchDiv | import torch
class TorchDiv(torch.nn.Module):
def __init__(self):
super(TorchDiv, self).__init__()
def forward(self, x, y):
return torch.div(x, y)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | ahangchen/torch2trt | TorchDiv | false | 6,121 | [
"MIT"
] | 1 | 53c663f0e0570ef7ffd6771354ae3478f63bd328 | https://github.com/ahangchen/torch2trt/tree/53c663f0e0570ef7ffd6771354ae3478f63bd328 |
SpaceToDepth | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torchvision import datasets as datasets
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data.distr... | Alibaba-MIIL/ZS_SDL | SpaceToDepth | false | 8,093 | [
"MIT"
] | 20 | 769fe4f57d2d458a7c4b5468a6395c9b296b1dad | https://github.com/Alibaba-MIIL/ZS_SDL/tree/769fe4f57d2d458a7c4b5468a6395c9b296b1dad |
BasicModel2 | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel2(nn.Module):
"""
Example model one from the paper
https://arxiv.org/pdf/1703.01365.pdf
f(x1, x2) = RELU(ReLU(x1) - 1 - ReLU(x2))
"""
def __init__(self):
super().__init__()
def forward... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | Europium248/captum | BasicModel2 | false | 416 | [
"BSD-3-Clause"
] | 0 | ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc | https://github.com/Europium248/captum/tree/ac02fae2651b8d68a44bcb9d03b91cbb3959f2fc |
NetVLAD | import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from sklearn.neighbors import NearestNeighbors
class NetVLAD(nn.Module):
"""NetVLAD layer implementation"""
def __init__(self, num_clusters=64, dim=128, normalize_input=True,
vladv2=False):
"""
Args:
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ByungHeeCha/visual_localization | NetVLAD | false | 17,431 | [
"BSD-3-Clause"
] | 3 | 787fb8f6ee5c6e69ece9e83a016d15596e5524bc | https://github.com/ByungHeeCha/visual_localization/tree/787fb8f6ee5c6e69ece9e83a016d15596e5524bc |
Mlp | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | LSH9832/MyPythonModules | Mlp | false | 755 | [
"MIT"
] | 0 | 442566a0fbd6ebe2bc20b6914686a1e2663d10c0 | https://github.com/LSH9832/MyPythonModules/tree/442566a0fbd6ebe2bc20b6914686a1e2663d10c0 |
decoder5 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | czczup/URST | decoder5 | false | 15,137 | [
"Apache-2.0"
] | 119 | 000ec9f7728f12ffad989ec1d07b1dd579514133 | https://github.com/czczup/URST/tree/000ec9f7728f12ffad989ec1d07b1dd579514133 |
DPLSTMCell | import math
import torch
from torch import Tensor
import torch.nn as nn
import torch.utils.data
import torch.utils.data.distributed
import torch.nn.parallel
from typing import Tuple
from typing import Optional
class RNNLinear(nn.Linear):
"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | anibadde/opacus | DPLSTMCell | false | 14,872 | [
"Apache-2.0"
] | 958 | be221231e1b579bdae4ad34c8ae0c7c4928cee25 | https://github.com/anibadde/opacus/tree/be221231e1b579bdae4ad34c8ae0c7c4928cee25 |
Conv2D | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | yhgon/NanoFlow | Conv2D | false | 16,763 | [
"BSD-3-Clause"
] | 62 | 73b24dfd4d607e73d6167897b83e9f61fcaaca3b | https://github.com/yhgon/NanoFlow/tree/73b24dfd4d607e73d6167897b83e9f61fcaaca3b |
StdConv2d | import torch
import torch.nn.functional as F
import torch.nn as nn
class StdConv2d(nn.Conv2d):
def forward(self, x):
w = self.weight
v, m = torch.var_mean(w, dim=[1, 2, 3], keepdim=True, unbiased=False)
w = (w - m) / torch.sqrt(v + 1e-05)
return F.conv2d(x, w, self.bias, self.stri... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | MetaMain/ViTRobust | StdConv2d | false | 17,712 | [
"BSD-3-Clause"
] | 6 | 5bca523f430933469d9f82022e334839388cee7a | https://github.com/MetaMain/ViTRobust/tree/5bca523f430933469d9f82022e334839388cee7a |
SpectralConvergence | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dy... | SolomidHero/speech-regeneration-enhancer | SpectralConvergence | false | 17,956 | [
"MIT"
] | 8 | eb43907ff085d68a707ff7bc3af14e93ff66fd65 | https://github.com/SolomidHero/speech-regeneration-enhancer/tree/eb43907ff085d68a707ff7bc3af14e93ff66fd65 |
DownsampleBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | XaviGurrola/RDUNet | DownsampleBlock | false | 9,646 | [
"MIT"
] | 0 | 549fc88c6faef1b310773944fc3988e22030d94d | https://github.com/XaviGurrola/RDUNet/tree/549fc88c6faef1b310773944fc3988e22030d94d |
SEBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | Keleas/Wafer_maps | SEBlock | false | 17,553 | [
"MIT"
] | 7 | ee555cafab213a86baf2d9e3b7fb392e1b89a832 | https://github.com/Keleas/Wafer_maps/tree/ee555cafab213a86baf2d9e3b7fb392e1b89a832 |
MultiLabelSoftBinaryCrossEntropy | import random
import torch
import torch.nn as nn
from random import random
import random
class MultiLabelSoftBinaryCrossEntropy(nn.Module):
def __init__(self, smooth_factor: 'float'=0, weighted: 'bool'=True, mcb:
'bool'=False, hp_lambda: 'int'=10, epsilon: 'float'=0.1, logits=
True, first_class_b... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | ClementPla/Retinal-Lesions-Segmentation | MultiLabelSoftBinaryCrossEntropy | false | 5,014 | [
"MIT"
] | 1 | 20fa4ac8eae24814470095bb6e7f08d6751c4e11 | https://github.com/ClementPla/Retinal-Lesions-Segmentation/tree/20fa4ac8eae24814470095bb6e7f08d6751c4e11 |
DecoderLayer | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | SeungoneKim/Transformer_implementation | DecoderLayer | false | 1,082 | [
"Apache-2.0"
] | 0 | a52bf552eb645fc9bfb812cc26842fc147d6c008 | https://github.com/SeungoneKim/Transformer_implementation/tree/a52bf552eb645fc9bfb812cc26842fc147d6c008 |
ScaledDotAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | yhgon/speedyspeech | ScaledDotAttention | false | 13,149 | [
"BSD-3-Clause"
] | 0 | 574c6a94091431f313e2aae8e154b8c80e6908ce | https://github.com/yhgon/speedyspeech/tree/574c6a94091431f313e2aae8e154b8c80e6908ce |
ResidualBlock | import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(channels, channels, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | elaina03/Single-Image-Dehazing | ResidualBlock | false | 3,463 | [
"MIT"
] | 0 | a6a29cb5591204f8066729df4053db0ea2b54aff | https://github.com/elaina03/Single-Image-Dehazing/tree/a6a29cb5591204f8066729df4053db0ea2b54aff |
PDCBlock_converted | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | arkel23/mmgeneration | PDCBlock_converted | false | 9,949 | [
"Apache-2.0"
] | 0 | 41a30e2972f2037f6aac60ed761bed3fe47bfe4d | https://github.com/arkel23/mmgeneration/tree/41a30e2972f2037f6aac60ed761bed3fe47bfe4d |
VGG16 | import torch
import numpy as np
import torchvision.transforms.functional as F
import torch.nn as nn
import torch.nn.functional as F
class Normalize:
def __init__(self, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)):
self.mean = mean
self.std = std
def undo(self, imgarr):
proc... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | loserbbb/1-stage-wseg | VGG16 | false | 15,992 | [
"Apache-2.0"
] | 364 | f1579be241986c1e19420bfbf6711b6c2208d99a | https://github.com/loserbbb/1-stage-wseg/tree/f1579be241986c1e19420bfbf6711b6c2208d99a |
softCrossEntropy | import torch
from torch import nn
from torch.nn import functional as F
class softCrossEntropy(nn.Module):
def __init__(self, reduce=True):
super(softCrossEntropy, self).__init__()
self.reduce = reduce
return
def forward(self, inputs, target):
"""
:param inputs: predic... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | Lingkai-Kong/Calibrated-BERT-Fine-Tuning | softCrossEntropy | false | 8,452 | [
"Apache-2.0"
] | 29 | 34b8dbf1bfb0d1e466621f149622933bfeab1555 | https://github.com/Lingkai-Kong/Calibrated-BERT-Fine-Tuning/tree/34b8dbf1bfb0d1e466621f149622933bfeab1555 |
Discriminator | import math
import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, n_hidden):
super(Discriminator, self).__init__()
self.weight = nn.Parameter(torch.Tensor(n_hidden, n_hidden))
self.reset_parameters()
def uniform(self, size, tensor):
bound = 1.0... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.a... | amazon-research/panrep | Discriminator | false | 18,301 | [
"Apache-2.0"
] | 10 | 57e6f71bb70c0908f3db28be97af0d818a863e19 | https://github.com/amazon-research/panrep/tree/57e6f71bb70c0908f3db28be97af0d818a863e19 |
SoftJaccardLoss | import torch
import torch.nn as nn
import torch.utils.data.distributed
from torch.backends import cudnn as cudnn
class SoftJaccardLoss(nn.Module):
"""SoftJaccard loss for binary problems.
"""
def __init__(self, use_log=False):
super(SoftJaccardLoss, self).__init__()
self.use_log = use_log... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data.distributed
from torch.backends import cudnn as cudnn
assert_size_stride = torch._C._dynamo.gu... | MIPT-Oulu/Collagen | SoftJaccardLoss | false | 17,658 | [
"MIT"
] | 4 | 0cbc4285d60e5c9fcc89f629fcf4321e80b7452c | https://github.com/MIPT-Oulu/Collagen/tree/0cbc4285d60e5c9fcc89f629fcf4321e80b7452c |
CombineSlices | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.utils.data.distributed
import torch.optim
assert_size_stride = torch._C._dynamo.gu... | Samuel-van-Gurp/fastMRI | CombineSlices | false | 2,858 | [
"MIT"
] | 0 | 0b1884a1c218961f81199144057ffcfde29a86ad | https://github.com/Samuel-van-Gurp/fastMRI/tree/0b1884a1c218961f81199144057ffcfde29a86ad |
DotRole | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.nn as nn
assert_size_stride = torch._C._dynamo.g... | OkYongChoi/smac | DotRole | false | 18,383 | [
"Apache-2.0"
] | 8 | 5b2b59e42d17a124e97feeecf9154a3a0aa9d260 | https://github.com/OkYongChoi/smac/tree/5b2b59e42d17a124e97feeecf9154a3a0aa9d260 |
PolarNet | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Spacider/comp9444_assignment | PolarNet | false | 2,846 | [
"Apache-2.0"
] | 0 | 149db9a562c579d03b3ea06c9de2020c8f3ef310 | https://github.com/Spacider/comp9444_assignment/tree/149db9a562c579d03b3ea06c9de2020c8f3ef310 |
BiAttention | import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
class BiAttention(nn.Module):
def __init__(self, input_size, dropout):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.input_linear = nn.Linear(input_size, 1, bias=False)
self.m... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Angelinaa/KOBE | BiAttention | false | 69 | [
"MIT"
] | 0 | 4d25487051e2791a977e59297f70a25e51806466 | https://github.com/Angelinaa/KOBE/tree/4d25487051e2791a977e59297f70a25e51806466 |
PixelNorm | import torch
from torch import nn
class PixelNorm(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input):
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=
True) + 1e-08)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Dolorousrtur/style-people | PixelNorm | false | 8,003 | [
"MIT"
] | 15 | c48b12b245cc50f8230c0654dffe40016f2a69f1 | https://github.com/Dolorousrtur/style-people/tree/c48b12b245cc50f8230c0654dffe40016f2a69f1 |
MLP | import torch
import torch.nn as nn
from torch.autograd import *
import torch.nn.parallel
import torch.utils.data
class FC(nn.Module):
def __init__(self, in_size, out_size, dropout_r=0.0, use_relu=True):
super(FC, self).__init__()
self.dropout_r = dropout_r
self.use_relu = use_relu
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | alfred100p/VC-R-CNN | MLP | false | 14,788 | [
"MIT"
] | 344 | c887f5b6db6932fb5c828c8037e299ce5baadb9e | https://github.com/alfred100p/VC-R-CNN/tree/c887f5b6db6932fb5c828c8037e299ce5baadb9e |
DotAttention | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | fmc123653/DeepKE | DotAttention | false | 15,403 | [
"MIT"
] | 676 | 4d30e51368681c7cb73e2ecacf9b922b441cbe99 | https://github.com/fmc123653/DeepKE/tree/4d30e51368681c7cb73e2ecacf9b922b441cbe99 |
FactoredAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Jovonni/jukebox | FactoredAttention | false | 735 | [
"MIT"
] | 0 | 965a6f78aae67506a6e4fcdb205e2c39132e12e0 | https://github.com/Jovonni/jukebox/tree/965a6f78aae67506a6e4fcdb205e2c39132e12e0 |
RSoftmax | import torch
from torch.nn import functional as F
import torch.nn as nn
import torch._C
import torch.serialization
from torch import optim as optim
class RSoftmax(nn.Module):
"""Radix Softmax module in ``SplitAttentionConv2d``.
Args:
radix (int): Radix of input.
groups (int): Groups of input.... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | Atten4Vis/DemystifyLocalViT | RSoftmax | false | 13,328 | [
"MIT"
] | 64 | 2e2327caec6d56ae2c8aa861b32bb62f3cdb786e | https://github.com/Atten4Vis/DemystifyLocalViT/tree/2e2327caec6d56ae2c8aa861b32bb62f3cdb786e |
GreedyTop1 | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as pt
import torch.distributed
import torch.distributed.elastic.multiprocessing.errors
assert_size_stride = torch._C._dynamo.gu... | SamuelLarkin/sockeye | GreedyTop1 | false | 9,530 | [
"Apache-2.0"
] | 0 | 7fcf6c96b15a887897aa712903ecf93c665ebddf | https://github.com/SamuelLarkin/sockeye/tree/7fcf6c96b15a887897aa712903ecf93c665ebddf |
LastLevelMaxPool | import torch
import torch.utils.data
from torchvision.transforms import functional as F
from torch import nn
import torch.nn.functional as F
class LastLevelMaxPool(nn.Module):
def forward(self, x):
return [F.max_pool2d(x, 1, 2, 0)]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | Amir4g/maskrcnn-benchmark | LastLevelMaxPool | false | 11,547 | [
"MIT"
] | 0 | c734fef962c3a2782e0055cfb6f825505a4b0c26 | https://github.com/Amir4g/maskrcnn-benchmark/tree/c734fef962c3a2782e0055cfb6f825505a4b0c26 |
ConstractiveLoss | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ConstractiveLoss(nn.Module):
def __init__(self, margin=2.0, dist_flag='l2'):
super(ConstractiveLoss, self).__init__()
self.margin = margin
self.dist_flag = dist_flag
def various_distance(self,... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
import to... | kensakurada/SceneChangeDet | ConstractiveLoss | false | 15,791 | [
"MIT"
] | 199 | 0530e0162863fec0c5296188526f0d27e0109814 | https://github.com/kensakurada/SceneChangeDet/tree/0530e0162863fec0c5296188526f0d27e0109814 |
TReLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | archiroid003/ICCV2019-LearningToPaint | TReLU | false | 12,110 | [
"MIT"
] | 0 | 4b5fc263e4843c159a61e5956956b3f7812693f8 | https://github.com/archiroid003/ICCV2019-LearningToPaint/tree/4b5fc263e4843c159a61e5956956b3f7812693f8 |
AEC | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | vdutell/biophys_encoder | AEC | false | 13,077 | [
"Apache-2.0"
] | 0 | 2ca8011338c4f1eb6b50e7cb74e07d105d1e9669 | https://github.com/vdutell/biophys_encoder/tree/2ca8011338c4f1eb6b50e7cb74e07d105d1e9669 |
ConvLayer | from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.nn.functional as f
class ConvLayer(nn.Conv3d):
def __init__(self, network_config, config, name, in_shape, groups=1):
self.name = name
self.layer_config = config
self.network_config = network_conf... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | superrrpotato/Spike-Train-Predict | ConvLayer | false | 4,402 | [
"MIT"
] | 0 | 0a924e5af11c2fc58cf9049a73fff00970a3c967 | https://github.com/superrrpotato/Spike-Train-Predict/tree/0a924e5af11c2fc58cf9049a73fff00970a3c967 |
GeneralizedDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
from typi... | JanSellner/MONAI | GeneralizedDiceLoss | false | 9,185 | [
"Apache-2.0"
] | 0 | ff8fa2bae94914030abb1bc0680417fdaa74afd8 | https://github.com/JanSellner/MONAI/tree/ff8fa2bae94914030abb1bc0680417fdaa74afd8 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.